ABSTRACT The remarkable yield of novel genetic associations over the last decade resulting from the agnostic approach of genome-wide association studies (GWAS) had not been matched by comparable advances on the environmental side. The exposome concept introduced by Chris Wild in 2005 as a comprehensive description of lifelong exposure history of external exposures (e.g., chemical, physical, and biological agents), general external environment (e.g., climate, urban-rural, socioeconomic position), and internal exposures (e.g., metabolites, gut microflora) has been operationalized in terms of the measurement of internal chemicals at particular points in time, typically using mass spectrometry to characterize the metabolome. With this machinery Environment-Wide Association Studies (EWAS) are now feasible, but there remain numerous methodological challenges before the EWAS concept can be considered a real companion to GWASs, including the dynamic nature of the external and internal environment, the problem of reverse causation, control of non-genetic host and environmental confounders, measurement error (temporal variability, instrument error, identification of unknown chemicals, etc.), and ways of conducting Gene-Environment-Wide Interaction Studies (GEWIS). An important component of the exposome is the microbiome. Evidence is mounting linking tumor promotion in a broad array of cancer types to the effects of bacterial microbiota. Local environmental conditions, affected by diet, antibiotics, pre- and probiotics, etc., could affect the structure of microbial communities, affecting risk of disease and response to therapy. The advent of high-throughput sequencing has allowed the relatively inexpensive identification and quantification of thousands of operational taxonomic units (OTUs) in a single biospecimen, providing a wealth of information on the complex structure of resident microbial communities. The microbiome raises many of the same methodological challenges as the exposome, such as time dependency, reverse causation, and non-genetic confounding, but also some different ones like ways of characterizing community effects like diversity and resilience. Although GxE and GxG are also relevant, equally interesting are host-microbial interactions and exposome-microbiome interactions. We propose an integrated approach to developing statistical methods for studying the determinants of the internal environment (the metabolome and the microbiome jointly) in relation to the external environment and the host genome and the relationship of the internal environment to disease risk. As part of this we propose to develop Bayesian network methods to relate all these variables and investigate mediation. We will apply our methods to data from, e.g., the Multi-Ethnic Cohort, the ColoCare Consortium, and a study of colorectal polyps in twins.